public BufferedImage getGrey() { ColorConvertOp ccp = new ColorConvertOp(ColorSpace.getInstance(ColorSpace.CS_GRAY), null); return image = ccp.filter(image, null); }
// Brighten using a linear formula that increases all color values public BufferedImage getBrighten() { RescaleOp rop = new RescaleOp(1.25f, 0, null); return image = rop.filter(image, null); }
// Blur by "convolving" the image with a matrix public BufferedImage getBlur() { float[] data = { .1111f, .1111f, .1111f, .1111f, .1111f, .1111f, .1111f, .1111f, .1111f, }; ConvolveOp cop = new ConvolveOp(new Kernel(3, 3, data)); return image = cop.filter(image, null);
}
// Sharpen by using a different matrix public BufferedImage getSharpen() { float[] data = { 0.0f, -0.75f, 0.0f, -0.75f, 4.0f, -0.75f, 0.0f, -0.75f, 0.0f }; ConvolveOp cop = new ConvolveOp(new Kernel(3, 3, data)); return image = cop.filter(image, null); }
// 11) Rotate the image 180 degrees about its center point public BufferedImage getRotate() { AffineTransformOp atop = new AffineTransformOp(AffineTransform.getRotateInstance(Math.PI, image.getWidth() / 2, image.getHeight() / 2), AffineTransformOp.TYPE_NEAREST_NEIGHBOR); return image = atop.filter(image, null); }
public BufferedImage getProcessedImg() { return image; }
public static void main(String[] args) throws IOException { FileInputStream fin = new FileInputStream("F:\\passCode.png"); BufferedImage bi = ImageIO.read(fin); MyImgFilter flt = new MyImgFilter(bi); flt.changeGrey(); flt.getGrey(); flt.getBrighten(); bi = flt.getProcessedImg();
https协议，现在换成了http协议。然后以前在第一步获取token是为了迷惑群众，在中途以前的返回码
200，硬是改成了一个304。不说多了 求教大家一个问题 以前铁道部的登录验证码没有在图片中加几根横线
非常好识别 Tesseract-OCR基本上10次就识别出来 然后现在加了几根横线 Tesseract-OCR基本上100次能中一次就不错了 。现在自己想了下 在网上弄了一个去掉图片中的横线的方法 但是一过滤图片就成了如下效果。<br/>有没有人能帮忙写个过滤算法 把横线过滤掉，字母保留成黑色？跪求！
如果有人解决，我愿意把我现在写好的抢票加上自动识别后贡献出去。让大家能在回家的时候抢到一张火车票。
附加过滤代码：
package org.image.test.one;
import java.awt.Image;
import java.awt.Toolkit;
import java.awt.color.ColorSpace;
import java.awt.geom.AffineTransform;
import java.awt.image.AffineTransformOp;
import java.awt.image.BufferedImage;
import java.awt.image.ColorConvertOp;
import java.awt.image.ColorModel;
import java.awt.image.ConvolveOp;
import java.awt.image.Kernel;
import java.awt.image.MemoryImageSource;
import java.awt.image.PixelGrabber;
import java.awt.image.RescaleOp;
import java.io.File;
import java.io.FileInputStream;
import java.io.IOException;
import javax.imageio.ImageIO;
public class MyImgFilter {
BufferedImage image;
private int iw, ih;
private int[] pixels;
public MyImgFilter(BufferedImage image) {
this.image = image;
iw = image.getWidth();
ih = image.getHeight();
pixels = new int[iw * ih];
}
/** 图像二值化 */
public BufferedImage changeGrey() {
PixelGrabber pg = new PixelGrabber(image.getSource(), 0, 0, iw, ih, pixels, 0, iw);
try {
pg.grabPixels();
} catch (InterruptedException e) {
e.printStackTrace();
}
// 设定二值化的域值，默认值为100
int grey = 100;
// 对图像进行二值化处理，Alpha值保持不变
ColorModel cm = ColorModel.getRGBdefault();
for (int i = 0; i < iw * ih; i++) {
int red, green, blue;
int alpha = cm.getAlpha(pixels[i]);
if (cm.getRed(pixels[i]) > grey) {
red = 255;
} else {
red = 0;
}
if (cm.getGreen(pixels[i]) > grey) {
green = 255;
} else {
green = 0;
}
if (cm.getBlue(pixels[i]) > grey) {
blue = 255;
} else {
blue = 0;
}
pixels[i] = alpha << 24 | red << 16 | green << 8 | blue; // 通过移位重新构成某一点像素的RGB值
}
// 将数组中的象素产生一个图像
Image tempImg = Toolkit.getDefaultToolkit().createImage(new MemoryImageSource(iw, ih, pixels, 0, iw));
image = new BufferedImage(tempImg.getWidth(null), tempImg.getHeight(null), BufferedImage.TYPE_INT_BGR);
image.createGraphics().drawImage(tempImg, 0, 0, null);
return image;
}
/** 中值滤波 */
public BufferedImage getMedian() {
PixelGrabber pg = new PixelGrabber(image.getSource(), 0, 0, iw, ih, pixels, 0, iw);
try {
pg.grabPixels();
} catch (InterruptedException e) {
e.printStackTrace();
}
// 对图像进行中值滤波，Alpha值保持不变
ColorModel cm = ColorModel.getRGBdefault();
for (int i = 1; i < ih - 1; i++) {
for (int j = 1; j < iw - 1; j++) {
int red, green, blue;
int alpha = cm.getAlpha(pixels[i * iw + j]);
// int red2 = cm.getRed(pixels[(i - 1) * iw + j]);
int red4 = cm.getRed(pixels[i * iw + j - 1]);
int red5 = cm.getRed(pixels[i * iw + j]);
int red6 = cm.getRed(pixels[i * iw + j + 1]);
// int red8 = cm.getRed(pixels[(i + 1) * iw + j]);
// 水平方向进行中值滤波
if (red4 >= red5) {
if (red5 >= red6) {
red = red5;
} else {
if (red4 >= red6) {
red = red6;
} else {
red = red4;
}
}
} else {
if (red4 > red6) {
red = red4;
} else {
if (red5 > red6) {
red = red6;
} else {
red = red5;
}
}
}
int green4 = cm.getGreen(pixels[i * iw + j - 1]);
int green5 = cm.getGreen(pixels[i * iw + j]);
int green6 = cm.getGreen(pixels[i * iw + j + 1]);
// 水平方向进行中值滤波
if (green4 >= green5) {
if (green5 >= green6) {
green = green5;
} else {
if (green4 >= green6) {
green = green6;
} else {
green = green4;
}
}
} else {
if (green4 > green6) {
green = green4;
} else {
if (green5 > green6) {
green = green6;
} else {
green = green5;
}
}
}
// int blue2 = cm.getBlue(pixels[(i - 1) * iw + j]);
int blue4 = cm.getBlue(pixels[i * iw + j - 1]);
int blue5 = cm.getBlue(pixels[i * iw + j]);
int blue6 = cm.getBlue(pixels[i * iw + j + 1]);
// int blue8 = cm.getBlue(pixels[(i + 1) * iw + j]);
// 水平方向进行中值滤波
if (blue4 >= blue5) {
if (blue5 >= blue6) {
blue = blue5;
} else {
if (blue4 >= blue6) {
blue = blue6;
} else {
blue = blue4;
}
}
} else {
if (blue4 > blue6) {
blue = blue4;
} else {
if (blue5 > blue6) {
blue = blue6;
} else {
blue = blue5;
}
}
}
pixels[i * iw + j] = alpha << 24 | red << 16 | green << 8 | blue;
}
}
// 将数组中的象素产生一个图像
Image tempImg = Toolkit.getDefaultToolkit().createImage(new MemoryImageSource(iw, ih, pixels, 0, iw));
image = new BufferedImage(tempImg.getWidth(null), tempImg.getHeight(null), BufferedImage.TYPE_INT_BGR);
image.createGraphics().drawImage(tempImg, 0, 0, null);
return image;
}
public BufferedImage getGrey() {
ColorConvertOp ccp = new ColorConvertOp(ColorSpace.getInstance(ColorSpace.CS_GRAY), null);
return image = ccp.filter(image, null);
}
// Brighten using a linear formula that increases all color values
public BufferedImage getBrighten() {
RescaleOp rop = new RescaleOp(1.25f, 0, null);
return image = rop.filter(image, null);
}
// Blur by "convolving" the image with a matrix
public BufferedImage getBlur() {
float[] data = { .1111f, .1111f, .1111f, .1111f, .1111f, .1111f, .1111f, .1111f, .1111f, };
ConvolveOp cop = new ConvolveOp(new Kernel(3, 3, data));
return image = cop.filter(image, null);
}
// Sharpen by using a different matrix
public BufferedImage getSharpen() {
float[] data = { 0.0f, -0.75f, 0.0f, -0.75f, 4.0f, -0.75f, 0.0f, -0.75f, 0.0f };
ConvolveOp cop = new ConvolveOp(new Kernel(3, 3, data));
return image = cop.filter(image, null);
}
// 11) Rotate the image 180 degrees about its center point
public BufferedImage getRotate() {
AffineTransformOp atop = new AffineTransformOp(AffineTransform.getRotateInstance(Math.PI, image.getWidth() / 2, image.getHeight() / 2), AffineTransformOp.TYPE_NEAREST_NEIGHBOR);
return image = atop.filter(image, null);
}
public BufferedImage getProcessedImg() {
return image;
}
public static void main(String[] args) throws IOException {
FileInputStream fin = new FileInputStream("F:\\passCode.png");
BufferedImage bi = ImageIO.read(fin);
MyImgFilter flt = new MyImgFilter(bi);
flt.changeGrey();
flt.getGrey();
flt.getBrighten();
bi = flt.getProcessedImg();
//String pname = args[0].substring(0, args[0].lastIndexOf("."));
File file = new File( "F:\\passCode.jpg");
ImageIO.write(bi, "jpg", file);
}
}Java code
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String path = "D:\\passCodeAction.jpg";
FileInputStream fin = new FileInputStream(path);
BufferedImage bi = ImageIO.read(fin);
MyImgFilter flt = new MyImgFilter(bi);
flt.changeGrey();
flt.getMedian();
flt.getGrey();
flt.getBrighten();
bi = flt.getProcessedImg();上面两张图片识别还是可以 但是遇到有些横线和字母的颜色相同的时候，有些字母就只能看到一个轮廓。